自己定义交叉熵损失函数和MSE损失函数并调用它们进行网络训练
时间: 2023-11-07 22:01:02 浏览: 40
交叉熵损失函数:
```python
import tensorflow as tf
def cross_entropy_loss(y_true, y_pred):
loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y_true, logits=y_pred))
return loss
```
MSE损失函数:
```python
import tensorflow as tf
def mse_loss(y_true, y_pred):
loss = tf.reduce_mean(tf.square(y_true - y_pred))
return loss
```
示例代码:
```python
import tensorflow as tf
from tensorflow.keras.layers import Dense, Flatten
from tensorflow.keras.models import Sequential
# create a simple model
model = Sequential([
Flatten(input_shape=(28,28)),
Dense(128, activation='relu'),
Dense(10)
])
# compile the model with cross entropy loss
model.compile(optimizer='adam', loss=cross_entropy_loss, metrics=['accuracy'])
# train the model
model.fit(x_train, y_train, epochs=10)
# compile the model with MSE loss
model.compile(optimizer='adam', loss=mse_loss, metrics=['accuracy'])
# train the model
model.fit(x_train, y_train, epochs=10)
```